Abstract
The word frequency analysis method is an important research method in the field of picture situation. With the development of picture science, word frequency analysis and other philological methods are also widely spread and used in China. Nowadays, it plays a fundamental role in the same kind of research, thus supporting more complex analysis methods such as social node network, clustering and strategic coordinates. As a well-known research method in the field of picture, scholars often ignore some basic problems or pay insufficient attention to the word frequency analysis method in application. Originally, the word frequency analysis method can break the discipline limit and be widely recognized and used by the academic circle, which is indeed beneficial to the development of the word frequency analysis method itself and the picture situation discipline. However, due to too familiar with and popularization, the academic circle nowadays pays too little attention to it, and even makes mistakes in application, which is a problem that cannot be ignored nowadays. Therefore, to grasp the development process of the word frequency analysis method in the application of China, to reveal the problems in the application of the word frequency analysis method in China, can provide useful reference for the future application of the word frequency analysis method in China’s scientific research; To make it can get more attention in the field of picture situation in China, better development and dissemination in the academic circle in China, which undoubtedly has a positive significance to promote the development of picture situation discipline in China.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, M.-F., Gao, X.-Y., Chou, T.-L.: Neighborhood frequency effect in Chinese word recognition: evidence from naming and lexical decision. J. Psycholinguist. Res. 46(1), 227–245 (2017)
Zhao, Q., Zhang, H., Zhao, X.: Fuzzy clustering image segmentation based on neighborhood constrained Gaussian mixture model. Pattern Recogn. Artif. Intell. 30(3), 214–223 (2017)
Merino, E.R., Castrillejo, F.M., Pin, J.D.: Neighborhood-based stopping criterion for contrastive divergence. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 2695–2704 (2018)
Nair, V.K.K., Biedermann, B., Nickels, L.: Understanding bilingual word learning: the role of phonotactic probability and phonological neighborhood density. J. Speech Lang. Hear. Res. 60(12), 1 (2017)
Sousa, C., Mason, W.A., Herrenkohl, T.I.: Direct and indirect effects of child abuse and environmental stress: a lifecourse perspective on adversity and depressive symptoms. American J. Orthopsychiatry 88(2), 180 (2017)
Rahman, M.T., Hosey, A., Guo, Z.: Systematic correlation and cell neighborhood analysis of SRAM PUF for robust and unique key generation. J. Hardware Syst. Secur. 1(2), 137–155 (2017)
Wang, D., Tan, X.: Bayesian neighborhood component analysis. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3140–3151 (2017)
Li, S., Liu, X., Chen, Y.: Measurement and performance analysis of functional neural network. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 35(2), 258–265 (2018)
Reinhart, W.F.: Athanassios Z Panagiotopoulos Multi-atom pattern analysis for binary superlattices. Soft Matter 13(38), 6803–6809 (2017)
Husain, S.S., Bober, M.: Improving large-scale image retrieval through robust aggregation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1783–1796 (2017)
Huang, H., Shen, F., Cai, Z.-Q.: Video abstract system based on spatial-temporal neighborhood trajectory analysis algorithm. Multimed. Tools Appl. 77(99), 1–18 (2018)
Deng, S., Tang, L., Zhang, X.: Research of adaptive neighborhood incremental principal component analysis and locality preserving projection manifold learning algorithm. J. Shanghai Jiaotong Univ. (Sci.) 23(2), 269–275 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, C. (2021). Word Frequency Analysis and Intelligent Word Recognition in Chinese Literature Based on Neighborhood Analysis. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51431-0_73
Download citation
DOI: https://doi.org/10.1007/978-3-030-51431-0_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-51430-3
Online ISBN: 978-3-030-51431-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)